Autonomous Motion Generation Based on Reliable Predictability
Shun Nishide*, Tetsuya Ogata*, Jun Tani**, Kazunori Komatani*, and Hiroshi G. Okuno*
*Graduate School of Informatics, Kyoto University
Engineering Building #10, Yoshida-honmachi, Sakyo-ku, Kyoto 606-8501, Japan
**Brain Science Institute, RIKEN
2-1 Hirosawa, Wako City, Saitama 351-0198, Japan
Predictability is an important factor for generating object manipulation motions. In this paper, the authors present a technique to generate autonomous object pushing motions based on object dynamics consistency, which is tightly connected to reliable predictability. The technique first creates an internal model of the robot and object dynamics using Recurrent Neural Network with Parametric Bias, based on transitions of extracted object features and generated robot motions acquired during active sensing experiences with objects. Next, the technique searches through the model for the most consistent object dynamics and corresponding robot motion through a consistency evaluation function using Steepest Descent Method. Finally, the initial static image of the object is linked to the acquired robot motion using a hierarchical neural network. The authors have conducted a motion generation experiment using pushing motions with cylindrical objects for evaluation of the method. The experiment has shown that the method has generalized its ability to adapt to object postures for generating consistent rolling motions.
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